Simulia

The SIMULIA CST Studio incorporates EMC solvers for applications across the EM spectrum into a single user interface. The solvers can be coupled to perform hybrid simulations, giving engineers the flexibility to analyse whole systems made up of multiple components in an efficient and straightforward way. Common Uses include the EM analysis of:

• Performance and Efficiency of Antennas and Filters
• Electromagnetic Compatibility and Interference
• Exposure of the Human Body to Electromagnetic Fields
• Electro-mechanical effects in Motors and Generators
• Thermal Effects in High-Power Devices

• High Frequency Solvers: Asymptotic, Eigenmode, Filter Designer 3D, Frequency Domain, Integral Equation, Multi-layer, Time Domain, and Hybrid Solver Task.
• Low Frequency Solvers: Electrostatic, Stationary Current, Magnetostatic, Low Frequency – Frequency Domain, and Low Frequency – Time Domain.
• Multiphysics Solvers: Thermal Steady State, Thermal Transient, Conjugate Heat Transfer, and Mechanical.
• Particle Solvers: Particle-in-Cell, Electrostatic Particle-in-Cell, Particle Tracking, and Wakefield.
• EMC and EDA Solvers: PDC, Rule Check, and Cable Harness.

Electromagnetic Systems Modelling with SIMULIA CST Studio

With System Assembly and Modelling (SAM), SIMULIA CST Studio provides an environment that simplifies the management of simulation projects, enabling the intuitive construction of electromagnetic (EM) systems and management of complex simulation flows using schematic modelling.

This framework can be used for analysing and optimising an entire device that consists of multiple individual components. These are described by relevant physical quantities such as currents, fields, or S-parameters. SAM enables the use of the most efficient solver technology for each component.

SIMULIA CST Studio contains several automatic optimisation algorithms, both local and global. Local optimisers provide fast convergence but risk converging to a local minimum rather than the overall best solution. On the other hand, global optimisers search the entire problem space but typically require more calculations.

These high-performance computing techniques can be used to speed up simulation and optimisation for very complex systems, or problems with large numbers of variables.

• Covariance Matrix Adaptation Evolutionary Strategy
• Trust Region Framework (TRF)
• Genetic Algorithm
• Particle Swam Optimisation
• Nelder Mead Simplex Algorithm
• Interpolated Quasi Newton
• Classic Powell
• Decap Optimisation